The present invention relates to image processing and, in particular, it concerns super-resolution image processing in which a plurality of overlapping sampled images are processed to generate one or more image of resolution and quality greater than that of the original sampled images.
The term “super-resolution” is used to refer generically to a group of techniques through which information from overlapping regions of multiple image frames can be used to improve the resolution of, and reduce noise and blurring in, a resultant image frame. In one super-resolution methodology, described in M. Irani, et al., “Motion Analysis For Image Enhancement Resolution, Occlusion, And Transparency,” Jour. Visual Communication And Image Representation, Vol. 4, No. 4, December, 1993, pp. 324-335, particularly section 3 thereof, a super-resolution methodology is described in which, starting with an initial guess as to an appropriate super-resolution image of a scene, a plurality of low-resolution images are generated and compared to actual low-resolution images which were recorded of the scene. By determining differences between the generated and actual low-resolution images, an error function is developed which is used in updating the initial guess super-resolution image. This process can be repeated through a series of iterations until a final super-resolution image is generated with enhanced resolution over the actual low-resolution images.
An essentially similar approach is also described in a paper by Assaf Zomet and Shmuel Peleg entitled “Efficient Super-Resolution and Application to Mosaics” (Proceedings of the International Conference on Pattern Recognition (ICPR), Barcelona, September 2000). The algorithm in question is there referred to as an “iterative back-projection” algorithm. Both the Irani et al. and Zomet et al. papers are hereby incorporated by reference and are believed to provide an understanding of the theoretical basis and general state of the art against which background the present invention may be better understood.
Practical implementations of the iterative back-projection methodology are typically computationally heavy and often suffer from problems of slow convergence, requiring relatively large numbers of iterations to reach an acceptable solution.
There is therefore a need for an implementation of iterative back-projection which would enhance the quality of the resulting super-resolution image and/or would enhance the rate of convergence of the algorithm towards an acceptable solution, thereby reducing the computational load and processing time for performing the super-resolution processing.